AI Agent Security Benchmarks

AI agent security benchmarks test whether agents remain safe as they use tools, retrieve information, manage memory, and act across multi-step workflows. Common approaches use sandboxed environments, realistic attack scenarios, adaptive red-teaming, and executable checks of tool calls or resulting system states; cyber ranges also assess vulnerability discovery, exploitation, and repair. The field is moving beyond single-turn prompt-injection tests to cover persistent memory, third-party skills, privacy, multi-agent interactions, and real-world interfaces. A central challenge is making results both realistic and comparable: static tests may overstate robustness, while attack success alone can miss harm severity, authorization failures, and the cost to legitimate task utility.